Healthcare facilities rely on patient monitoring to supplement interventions and reduce the instances of patient falls. Constant eyes-on monitoring of patients can be difficult for healthcare professionals to maintain. Video monitoring can be used to automate patient monitoring and increase the ability of a healthcare professional to effectively monitor a group of patients distributed between different rooms. Various systems and methods for patient video monitoring have been disclosed, such as U.S. Patent Application No. 2009/0278934 entitled System and Method for Predicting Patient Falls, U.S. Patent Application No. 2010/0134609 entitled System and Method for Documenting Patient Procedures; U.S. Patent Application No. 2012/0026308 entitled System and Method for Using a Video Monitoring System to Prevent and Manage Decubitus Ulcers in Patients, and U.S. Provisional Patent Application No. 61/707,227 entitled System and Method for Monitoring a Fall State of a Patient and Minimizing False Alarms.
Various routines can be run by a monitoring system to automatically detect patient events. For example, a system can monitor a patient in a bed and issue an alert if the patient falls or otherwise leaves the bed. Monitoring systems have generally used cameras that monitor patients in two dimensions, typically reducing a scene to a flat image. Various features and algorithms have been developed to accurately monitor patient events occurring in three dimensions with cameras that reduce the scenes to two dimensions. For example, a two dimensional camera can be set up to view a scene that includes a hospital bed. A user can identify one or more zones within the scene associated with risk to the patient. For example, the zones can be aligned with the edges of the bed where a patient is at risk of falling from the bed. An algorithm can then process image information within the zones over time to detect changes within the zones indicative of patient movement. Such systems can be effective in patient monitoring but also can have several limitations. For example, the two dimensional images may lack depth information such that shadows can be interpreted as patient movement. Also, because the zones may be aligned with a bed or other area, changes to the scene (e.g., movement of the bed) may require that the zones be realigned. Boundaries between similarly colored areas at different depths can be difficult to detect using two dimensional techniques. There is a need for monitoring systems that can interpret scenes in three dimensions and automatically adapt to changes in the scenes.